如何closuresPySpark中的INFO日志logging？
我使用AWS EC2指南安装了Spark，并且可以使用bin/pyspark脚本启动程序，以获得火花提示，还可以成功执行Quick Start查询。
但是，我不能在我的生活中弄清楚如何在每个命令之后停止所有冗长的INFO日志logging。
我在下面的代码中尝试了几乎所有可能的场景（注释掉，设置为OFF），在我从启动应用程序的位置以及每个节点的conf文件夹的log4j.properties文件中，什么都不做。 执行完每条语句后，仍然会打印日志INFO语句。
我很困惑这是如何工作的。
#Set everything to be logged to the console log4j.rootCategory=INFO, console log4j.appender.console=org.apache.log4j.ConsoleAppender log4j.appender.console.target=System.err log4j.appender.console.layout=org.apache.log4j.PatternLayout log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n # Settings to quiet third party logs that are too verbose log4j.logger.org.eclipse.jetty=WARN log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO
当我使用SPARK_PRINT_LAUNCH_COMMAND时，这是我完整的类path：
Spark命令：/Library/Java/JavaVirtualMachines/jdk1.8.0_05.jdk/Contents/Home/bin/java -cp：/root/spark-1.0.1-bin-hadoop2/conf：/root/spark-1.0.1 -bin-hadoop2 / CONF：/root/spark-1.0.1-bin-hadoop2/lib/spark-assembly-1.0.1-hadoop2.2.0.jar：/root/spark-1.0.1-bin-hadoop2/lib /datanucleus-api-jdo-3.2.1.jar:/root/spark-1.0.1-bin-hadoop2/lib/datanucleus-core-3.2.2.jar:/root/spark-1.0.1-bin-hadoop2 /lib/datanucleus-rdbms-3.2.1.jar -XX：MaxPermSize = 128m -Djava.library.path = -Xms512m -Xmx512m org.apache.spark.deploy.SparkSubmit spark-shell –class org.apache.spark。 repl.Main
spark-env.sh内容：
#!/usr/bin/env bash # This file is sourced when running various Spark programs. # Copy it as spark-env.sh and edit that to configure Spark for your site. # Options read when launching programs locally with # ./bin/run-example or ./bin/spark-submit # - HADOOP_CONF_DIR, to point Spark towards Hadoop configuration files # - SPARK_LOCAL_IP, to set the IP address Spark binds to on this node # - SPARK_PUBLIC_DNS, to set the public dns name of the driver program # - SPARK_CLASSPATH=/root/spark-1.0.1-bin-hadoop2/conf/ # Options read by executors and drivers running inside the cluster # - SPARK_LOCAL_IP, to set the IP address Spark binds to on this node # - SPARK_PUBLIC_DNS, to set the public DNS name of the driver program # - SPARK_CLASSPATH, default classpath entries to append # - SPARK_LOCAL_DIRS, storage directories to use on this node for shuffle and RDD data # - MESOS_NATIVE_LIBRARY, to point to your libmesos.so if you use Mesos # Options read in YARN client mode # - HADOOP_CONF_DIR, to point Spark towards Hadoop configuration files # - SPARK_EXECUTOR_INSTANCES, Number of workers to start (Default: 2) # - SPARK_EXECUTOR_CORES, Number of cores for the workers (Default: 1). # - SPARK_EXECUTOR_MEMORY, Memory per Worker (eg 1000M, 2G) (Default: 1G) # - SPARK_DRIVER_MEMORY, Memory for Master (eg 1000M, 2G) (Default: 512 Mb) # - SPARK_YARN_APP_NAME, The name of your application (Default: Spark) # - SPARK_YARN_QUEUE, The hadoop queue to use for allocation requests (Default: 'default') # - SPARK_YARN_DIST_FILES, Comma separated list of files to be distributed with the job. # - SPARK_YARN_DIST_ARCHIVES, Comma separated list of archives to be distributed with the job. # Options for the daemons used in the standalone deploy mode: # - SPARK_MASTER_IP, to bind the master to a different IP address or hostname # - SPARK_MASTER_PORT / SPARK_MASTER_WEBUI_PORT, to use non-default ports for the master # - SPARK_MASTER_OPTS, to set config properties only for the master (eg "-Dx=y") # - SPARK_WORKER_CORES, to set the number of cores to use on this machine # - SPARK_WORKER_MEMORY, to set how much total memory workers have to give executors (eg 1000m, 2g) # - SPARK_WORKER_PORT / SPARK_WORKER_WEBUI_PORT, to use non-default ports for the worker # - SPARK_WORKER_INSTANCES, to set the number of worker processes per node # - SPARK_WORKER_DIR, to set the working directory of worker processes # - SPARK_WORKER_OPTS, to set config properties only for the worker (eg "-Dx=y") # - SPARK_HISTORY_OPTS, to set config properties only for the history server (eg "-Dx=y") # - SPARK_DAEMON_JAVA_OPTS, to set config properties for all daemons (eg "-Dx=y") # - SPARK_PUBLIC_DNS, to set the public dns name of the master or workers export SPARK_SUBMIT_CLASSPATH="$FWDIR/conf"
只需在spark目录下执行这个命令：
cp conf/log4j.properties.template conf/log4j.properties
编辑log4j.properties：
# Set everything to be logged to the console log4j.rootCategory=INFO, console log4j.appender.console=org.apache.log4j.ConsoleAppender log4j.appender.console.target=System.err log4j.appender.console.layout=org.apache.log4j.PatternLayout log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n # Settings to quiet third party logs that are too verbose log4j.logger.org.eclipse.jetty=WARN log4j.logger.org.eclipse.jetty.util.component.AbstractLifeCycle=ERROR log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO
在第一行replace：
log4j.rootCategory=INFO, console
通过：
log4j.rootCategory=WARN, console
保存并重新启动你的shell。 它适用于OS X上的Spark 1.1.0和Spark 1.5.1。
受到了pyspark / tests.py的启发，我做到了
def quiet_logs( sc ): logger = sc._jvm.org.apache.log4j logger.LogManager.getLogger("org"). setLevel( logger.Level.ERROR ) logger.LogManager.getLogger("akka").setLevel( logger.Level.ERROR )
在创buildSparkContext之后调用这个方法减less了logging我的testfrom 2647到163.的stderr行。但是，创buildSparkContext本身的日志163，直到
15/08/25 10:14:16 INFO SparkDeploySchedulerBackend: SchedulerBackend is ready for scheduling beginning after reached minRegisteredResourcesRatio: 0.0
我不清楚如何以编程方式调整这些。
编辑您的conf / log4j.properties文件并更改以下行：
log4j.rootCategory=INFO, console
至
log4j.rootCategory=ERROR, console
另一种方法是：
popupFire-shell并键入以下内容：
import org.apache.log4j.Logger import org.apache.log4j.Level Logger.getLogger("org").setLevel(Level.OFF) Logger.getLogger("akka").setLevel(Level.OFF)
之后你将不会看到任何日志。
>>> log4j = sc._jvm.org.apache.log4j >>> log4j.LogManager.getRootLogger().setLevel(log4j.Level.ERROR)
您还可以使用sc.setLogLevel("FATAL")在脚本中设置日志级别。 从文档 ：
控制我们的logLevel。 这将覆盖任何用户定义的日志设置。 有效的日志级别包括：ALL，DEBUG，ERROR，FATAL，INFO，OFF，TRACE，WARN
这可能是由于Spark如何计算其类path。 我的直觉是，Hadoop的log4j.properties文件出现在类path的Spark之前，阻止你的更改生效。
如果你跑步
SPARK_PRINT_LAUNCH_COMMAND=1 bin/spark-shell
那么Spark将打印用于启动shell的完整类path; 就我而言，我明白了
Spark Command: /usr/lib/jvm/java/bin/java -cp :::/root/ephemeral-hdfs/conf:/root/spark/conf:/root/spark/lib/spark-assembly-1.0.0-hadoop1.0.4.jar:/root/spark/lib/datanucleus-api-jdo-3.2.1.jar:/root/spark/lib/datanucleus-core-3.2.2.jar:/root/spark/lib/datanucleus-rdbms-3.2.1.jar -XX:MaxPermSize=128m -Djava.library.path=:/root/ephemeral-hdfs/lib/native/ -Xms512m -Xmx512m org.apache.spark.deploy.SparkSubmit spark-shell --class org.apache.spark.repl.Main
其中/root/ephemeral-hdfs/conf位于类path的头部。
我已经打开了一个问题[SPARK-2913]在下一个版本中解决这个问题（我很快就会有一个补丁）。
与此同时，这里有几个解决方法：
- 将
export SPARK_SUBMIT_CLASSPATH="$FWDIR/conf"到spark-env.sh。 - 删除（或重命名）
/root/ephemeral-hdfs/conf/log4j.properties。
在Spark 2.0中，您还可以使用setLogLevel为您的应用程序dynamicconfiguration它：
from pyspark.sql import SparkSession spark = SparkSession.builder.\ master('local').\ appName('foo').\ getOrCreate() spark.sparkContext.setLogLevel('WARN')
在pyspark控制台中，默认的spark会话已经可用。
我在Amazon EC2中使用了1个主控和2个从属以及Spark 1.2.1。
# Step 1. Change config file on the master node nano /root/ephemeral-hdfs/conf/log4j.properties # Before hadoop.root.logger=INFO,console # After hadoop.root.logger=WARN,console # Step 2. Replicate this change to slaves ~/spark-ec2/copy-dir /root/ephemeral-hdfs/conf/
我这样做的方式是：
在我运行spark-submit脚本的位置
$ cp /etc/spark/conf/log4j.properties . $ nano log4j.properties
将INFO更改为所需的日志logging级别，然后运行spark-submit
我想继续使用日志logging（Python的日志logging工具），可以尝试为应用程序和Spark分割configuration：
LoggerManager() logger = logging.getLogger(__name__) loggerSpark = logging.getLogger('py4j') loggerSpark.setLevel('WARNING')
